Computational Modeling Approaches to Finance and Fintech Innovation

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: closed (22 September 2022) | Viewed by 55175

Special Issue Editors


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Guest Editor
Gabelli School of Business, Fordham University, New York, NY 10023, USA
Interests: digital transformation; digital platforms; network effects; economics of technology; dynamics of complex systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Worcester Polytechnic Institute, Worcester, MA 01609, USA
Interests: system dynamics; computational economics; higher education; computational social science; simulations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed the tremendous growth of financial and fintech innovations that are transforming financial services, financial markets and the global economy.

We invite high-quality research submissions that study all aspects of finance and fintech innovation. Methodologically, we are especially interested in computational modeling and simulation approaches, including system dynamics, agent-based modeling, network models, machine learning, natural language processing, etc. We encourage interdisciplinary research that appreciates complex systems and seeks to understand, explain, design and/or forecast system behavior. The research should have clear practical implications and it should help managers, regulators and policy-makers make better decisions and create more value, while navigating the complex fintech landscape and its implications.

A list of suggested topics includes the following:

  • Trading and algorithmic trading
  • AI/machine learning in banking
  • Blockchains and applications
  • Smart contracts
  • Investment advice and robo-advisers
  • Fintech applications
  • Payment systems
  • Bitcoin, cryptocurrencies, CBDC, digital assets, NFTs
  • Decentralized finance
  • Designing fintech products and customer experience
  • Fintech and financial markets
  • Fintech startups
  • Digitalization and Digital transformation of financial services firms and markets
  • Social media, Cloud, Mobile, IoT, AR/VR and fintech
  • Big data, predictive analytics, data visualization in financial services
  • Financial and risk analytics
  • Open banking and APIs
  • Platforms and ecosystems
  • Crowdfunding
  • P2P lending
  • Fintech and cybersecurity
  • BigTech and finance
  • Dynamics of financial instability
  • Fintech economic and social impact
  • Fintech for good (social finance, green finance, social innovation, financial inclusion, responsible investing etc.)
  • Regulation of fintech and Regtech
  • Covid-19 and fintech

We also encourage submissions on other topics related to the theme of the Special Issue.

Prof. Evangelos Katsamakas
Prof. Oleg Pavlov
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

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Published Papers (16 papers)

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Research

20 pages, 2332 KiB  
Article
Predicting Multi-Period Corporate Default Based on Bayesian Estimation of Forward Intensity—Evidence from China
by Zhengfang Ni, Minghui Jiang and Wentao Zhan
Systems 2023, 11(1), 18; https://doi.org/10.3390/systems11010018 - 31 Dec 2022
Cited by 1 | Viewed by 1312
Abstract
We employed a forward intensity approach to predict the multi-period defaults of Chinese-listed firms during the period 2001–2019 on a monthly basis. We introduced the firm’s default heterogeneity into the model, and each firm’s actual past default situation was considered for Bayesian estimation. [...] Read more.
We employed a forward intensity approach to predict the multi-period defaults of Chinese-listed firms during the period 2001–2019 on a monthly basis. We introduced the firm’s default heterogeneity into the model, and each firm’s actual past default situation was considered for Bayesian estimation. Maximum pseudo-likelihood estimation was conducted on 3513 firms to calculate the parameters of the Bayesian model to adjust the default intensity of all 4216 firms. Finally, we re-calculated the default probabilities and compared them with the original default probabilities of the out-of-sample 703 firms for all prediction horizons. We found that the Bayesian model, considering the firm’s default heterogeneity, improved the prediction accuracy ratio of the out-of-sample firm’s default probabilities both for short and long horizons. As compared with the original model, the prediction accuracy ratio of the out-of-sample’s default probabilities, which were computed by our model, increased by almost 15% for horizons from 1 month to 6 months. When the horizon was extended from 1 year to 3 years, the prediction accuracy ratio increased by more than 10%. We found that the Bayesian model improved the predictive performance of the forward intensity model, which is helpful to improve the credit risk measurement system of Chinese-listed firms. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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25 pages, 1923 KiB  
Article
Structural Shocks, Business Condition Expectations, and Expected Stock Market Returns
by Weizhong Chen and Mingming Liu
Systems 2022, 10(6), 228; https://doi.org/10.3390/systems10060228 - 21 Nov 2022
Viewed by 1330
Abstract
Through the interaction terms of business condition expectations and structural shocks, the non-linear effects of business condition expectations on expected stock market returns were studied. We found that the recession expectation enlarges the positive effects of a permanent shock on the expected stock [...] Read more.
Through the interaction terms of business condition expectations and structural shocks, the non-linear effects of business condition expectations on expected stock market returns were studied. We found that the recession expectation enlarges the positive effects of a permanent shock on the expected stock market return, and also increases the negative impacts of the temporary shock. Over the long-horizon forecast, these effects increase over time. Moreover, the impacts under the recession expectation are greater than those under the expansion expectation. The results are robust and have economic significance. We also provide evidence for the existence of a negative relationship between business condition expectations and expected stock market returns. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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23 pages, 3055 KiB  
Article
Early Warning of Systemic Financial Risk of Local Government Implicit Debt Based on BP Neural Network Model
by Yinglan Zhao, Yi Li, Chen Feng, Chi Gong and Hongru Tan
Systems 2022, 10(6), 207; https://doi.org/10.3390/systems10060207 - 4 Nov 2022
Cited by 8 | Viewed by 2682
Abstract
In recent years, local governments have boosted their local economies by raising large amounts of debt. Even though the state further strictly controls local government debt, the hidden debt formed by the local government borrowing in disguised form can infect systemic financial risks, [...] Read more.
In recent years, local governments have boosted their local economies by raising large amounts of debt. Even though the state further strictly controls local government debt, the hidden debt formed by the local government borrowing in disguised form can infect systemic financial risks, creating an urgent need to carry out risk warning based on local government hidden debt. The paper uses the macro indicators of local government implicit debt risk at the prefecture-level city level, and introduces the micro indicators of PPP projects, financing platform bank debt, and urban investment debt to establish a BP neural network model. We not only study the contagion effect of local government hidden debt on systemic financial risks, but also predict the systemic financial risks in 2019 and construct an early warning risk system based on the prefecture-level city data from 2015 to 2018. In addition, the early warning effect of local government implicit debt on systemic financial risk under different stress scenarios is investigated. The study found that the implicit debt risk of local governments, the scale of financing platform bank debt, the scale of PPP, and the scale of urban investment bonds have a significant impact on systemic financial risks. The neural network model constructed by introducing these four variables at the same time can better predict the level of systemic financial risk. The model can also accurately predict the changes in systemic financial risks under the stress test of the increase in hidden debt of different local governments, and has a good early warning effect. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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24 pages, 1985 KiB  
Article
Modeling the Enablers to FinTech Innovation in Saudi Arabia: A Hybrid Approach Using ISM and ANP
by Anas A. Makki and Ammar Y. Alqahtani
Systems 2022, 10(5), 181; https://doi.org/10.3390/systems10050181 - 8 Oct 2022
Cited by 8 | Viewed by 3359
Abstract
Financial technology (FinTech) has emerged as a significant financial sector breakthrough due to the sharing economy, new legislation, and IT advances, contributing to its fast growth. Under a new national policy, Saudi Arabia intends to increase the number of FinTech firms. Thus, it [...] Read more.
Financial technology (FinTech) has emerged as a significant financial sector breakthrough due to the sharing economy, new legislation, and IT advances, contributing to its fast growth. Under a new national policy, Saudi Arabia intends to increase the number of FinTech firms. Thus, it is necessary to develop a more profound understanding of what critically enables FinTech innovation, how these enablers are interconnected, and their priorities. This research study aims to identify and model the critical enablers of FinTech innovation by exploring contextual relationships among them and their importance. A hybrid approach was followed using interpretive structural modeling (ISM) and an analytic network process (ANP) to achieve the objective. Eleven enablers and their essential components were extracted from the literature and confirmed by Saudi FinTech experts who provided input data on their linkages and relative importance through interviews and a designed questionnaire. The developed model reveals the enablers’ structure in terms of their driving and dependence powers and classifies them into six levels with relative importance to each other. The developed model in this research puts forward a holistic perspective on FinTech and innovation, assisting decision-makers, regulators, policy designers, practitioners, and technology developers to create effective ways to safeguard the FinTech industry’s growth. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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21 pages, 3760 KiB  
Article
The Strategy Selection in Financial Fraud and Audit Supervision: A Study Based on a Three-Party Evolutionary Game Model
by Binghui Wu, Jing Yang, Guanhao Fu and Mengjiao Zhang
Systems 2022, 10(5), 173; https://doi.org/10.3390/systems10050173 - 29 Sep 2022
Cited by 4 | Viewed by 2971
Abstract
In recent years, financial fraud of listed internet companies has emerged one after another. Under the strategic goal of full audit coverage, the problems caused by audit failure have attracted more and more attention in China. We use the bounded rationality theory to [...] Read more.
In recent years, financial fraud of listed internet companies has emerged one after another. Under the strategic goal of full audit coverage, the problems caused by audit failure have attracted more and more attention in China. We use the bounded rationality theory to analyze the strategy selections of listed internet companies, accounting firms, and audit regulators, and put forward three hypotheses: the hypothesis of participants, the hypothesis of strategy selections, and the hypothesis of model parameters. Next, the three-party evolutionary game model is constructed, and only one stable equilibrium point is found. In numerical simulation analyses, we discuss the strategy selections of the three parties under the impact of different model parameters. The research framework of this paper enriches the existing research on financial fraud and audit supervision and deepens the evolutionary mechanism of three-party strategy selections. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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21 pages, 5923 KiB  
Article
Network Formation and Financial Inclusion in P2P Lending: A Computational Model
by Evangelos Katsamakas and J. Manuel Sánchez-Cartas
Systems 2022, 10(5), 155; https://doi.org/10.3390/systems10050155 - 15 Sep 2022
Cited by 7 | Viewed by 3622
Abstract
What characteristics of fintech lending platforms improve access to funding and increase financial inclusion? We build a computational model of platform lending that is used to study the endogenous loan network formation process on the platform. Given the multidimensional nature of financial inclusion, [...] Read more.
What characteristics of fintech lending platforms improve access to funding and increase financial inclusion? We build a computational model of platform lending that is used to study the endogenous loan network formation process on the platform. Given the multidimensional nature of financial inclusion, we address what factors influence the number of loans, the level of investment/debt, and how those relate to the distribution of investment/debt across agents. We find that platform scale and SME reach are essential in determining the number of loans on the platform. However, the willingness to accept risks is the main driver behind the value of those loans. We also find that increased platform scale, high-risk thresholds, and low-interest rates lead to more evenly distributed investments. Moreover, we find that large platforms help increase diversity and lead to a more evenly distributed power among peers. We conclude that digital platforms increase financial inclusion, helping to foster investment and achieve a more egalitarian allocation of resources. These results can guide new theory development about the impact of P2P lending on inequality as well as help platforms to promote financial inclusion. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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16 pages, 2213 KiB  
Article
A Two-Staged SEM-Artificial Neural Network Approach to Analyze the Impact of FinTech Adoption on the Sustainability Performance of Banking Firms: The Mediating Effect of Green Finance and Innovation
by Chen Yan, Abu Bakkar Siddik, Li Yong, Qianli Dong, Guang-Wen Zheng and Md Nafizur Rahman
Systems 2022, 10(5), 148; https://doi.org/10.3390/systems10050148 - 8 Sep 2022
Cited by 48 | Viewed by 6125
Abstract
This study aims to examine the effect of FinTech adoption on the sustainability performance of banking institutions in an emerging economy such as Bangladesh. Besides, this study also investigates the mediating role of green finance and green innovation in the relationship between FinTech [...] Read more.
This study aims to examine the effect of FinTech adoption on the sustainability performance of banking institutions in an emerging economy such as Bangladesh. Besides, this study also investigates the mediating role of green finance and green innovation in the relationship between FinTech adoption and sustainability performance. To examine the relationship among the study variables, this study used data from 351 employees of banking institutions operating in Bangladesh during the period January to March 2021 using a convenience sampling method. Furthermore, the study utilized a two-staged structural equation modeling and an artificial neural network (SEM-ANN) approach to analyze the data. The findings show that FinTech adoption significantly influences green finance, green innovation, and sustainability performance. Similarly, the results indicate that green finance and green innovation have a significant positive influence on sustainability performance. Furthermore, the results reveal that green finance and green innovation fully mediate the relationship between FinTech adoption and the sustainability performance of banking institutions. Moreover, the present study contributes to the existing literature on technological innovation, green finance, and sustainability performance greatly as it is the first study to examine both linear and non-linear relationships among these variables using the SEM-ANN approach. As a result, the study highlights the importance of FinTech adoption, green finance, and innovation in the attainment of sustainability performance, as well as the urgent need to incorporate new technologies, green initiatives, and financing into banking strategies to help achieve the country’s sustainable economic development. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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20 pages, 6677 KiB  
Article
Applications of Markov Decision Process Model and Deep Learning in Quantitative Portfolio Management during the COVID-19 Pandemic
by Han Yue, Jiapeng Liu and Qin Zhang
Systems 2022, 10(5), 146; https://doi.org/10.3390/systems10050146 - 8 Sep 2022
Cited by 3 | Viewed by 2809
Abstract
Whether for institutional investors or individual investors, there is an urgent need to explore autonomous models that can adapt to the non-stationary, low-signal-to-noise markets. This research aims to explore the two unique challenges in quantitative portfolio management: (1) the difficulty of representation and [...] Read more.
Whether for institutional investors or individual investors, there is an urgent need to explore autonomous models that can adapt to the non-stationary, low-signal-to-noise markets. This research aims to explore the two unique challenges in quantitative portfolio management: (1) the difficulty of representation and (2) the complexity of environments. In this research, we suggest a Markov decision process model-based deep reinforcement learning model including deep learning methods to perform strategy optimization, called SwanTrader. To achieve better decisions of the portfolio-management process from two different perspectives, i.e., the temporal patterns analysis and robustness information capture based on market observations, we suggest an optimal deep learning network in our model that incorporates a stacked sparse denoising autoencoder (SSDAE) and a long–short-term-memory-based autoencoder (LSTM-AE). The findings in times of COVID-19 show that the suggested model using two deep learning models gives better results with an alluring performance profile in comparison with four standard machine learning models and two state-of-the-art reinforcement learning models in terms of Sharpe ratio, Calmar ratio, and beta and alpha values. Furthermore, we analyzed which deep learning models and reward functions were most effective in optimizing the agent’s management decisions. The results of our suggested model for investors can assist in reducing the risk of investment loss as well as help them to make sound decisions. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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26 pages, 3476 KiB  
Article
Effective Evaluation of Green and High-Quality Development Capabilities of Enterprises Using Machine Learning Combined with Genetic Algorithm Optimization
by Dongxue Zhai, Xuefeng Zhao, Yanfei Bai and Delin Wu
Systems 2022, 10(5), 128; https://doi.org/10.3390/systems10050128 - 24 Aug 2022
Cited by 6 | Viewed by 1740
Abstract
Studying the impact of green and high-quality development is of great significance to the healthy growth and sustainable development of enterprises. This paper discusses the influencing factors of the green and high-quality development of enterprises from the perspective of ownership structure and innovation [...] Read more.
Studying the impact of green and high-quality development is of great significance to the healthy growth and sustainable development of enterprises. This paper discusses the influencing factors of the green and high-quality development of enterprises from the perspective of ownership structure and innovation ability, aiming to clarify the impact mechanism of these influencing factors on the green development of enterprises, and combined with emerging machine learning technologies, to propose a novel and effective corporate green high-quality development using a regression prediction model for quality development. Linear regression and one-way ANOVA were used to analyze the influence of each variable on the green and high-quality development of the enterprise, and the weight proportions of each influencing factor under the linear model were obtained. Two machine learning models based on the random forest (RF) algorithm and support vector machine algorithm were established, and the random parameters in the two machine learning algorithms were optimized by a genetic algorithm (GA). The reliability and accuracy of machine learning models and multivariate linear models were compared. The results show that the GA–RF model has superior regression performance compared with other prediction models. This paper provides a convenient machine learning model, which can quickly and effectively predict the green and high-quality development of enterprises, and provide help for enterprise decision-making and government policy formulation. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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19 pages, 3254 KiB  
Article
Taxation of Fiat Money Using Dynamic Control
by Khalid Saeed
Systems 2022, 10(3), 84; https://doi.org/10.3390/systems10030084 - 19 Jun 2022
Cited by 2 | Viewed by 2534
Abstract
The treasury analogy is widely used in most countries in the execution of fiscal policies striving to match taxation with public expenditure while, at the same time, the central banks tinker with interest rates and reserve requirements and conduct open market operations for [...] Read more.
The treasury analogy is widely used in most countries in the execution of fiscal policies striving to match taxation with public expenditure while, at the same time, the central banks tinker with interest rates and reserve requirements and conduct open market operations for regulating money supply. This paper investigates the relevance of those policy actions to the ubiquitous use of fiat money tokens which in addition to meeting public expenditure and paying taxes also facilitate transactions in the economy. A parsimonious model of the macroeconomic system subsuming the prevalent fiscal and monetary interventions is developed and experimented with to explore alternative policy levers for funding public expenditure and controlling money supply. A novel dynamic control regimen to regulate taxation and open market operations for controlling inflation is proposed. It is shown that while the public spending can be entirely met through new money creation, taxation and open market operations must be used as inflation control instruments, whereas expenditure instruments can be used to alleviate the regressivity of taxation regimen. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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19 pages, 2104 KiB  
Article
Nonlinear Dependence and Spillovers between Currency Markets and Global Economic Variables
by Zhuhua Jiang, Jose Arreola Hernandez, Ron P. McIver and Seong-Min Yoon
Systems 2022, 10(3), 80; https://doi.org/10.3390/systems10030080 - 9 Jun 2022
Cited by 2 | Viewed by 1994
Abstract
The widespread integration and growing systemic dependence among currency, stock, and commodity markets render these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. [...] Read more.
The widespread integration and growing systemic dependence among currency, stock, and commodity markets render these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we aim to explore the spillovers and nonlinear dependencies between the seven major foreign exchange rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by employing a directional spillover network approach. We also use a multi-scale decomposition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. The major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the Euro–Dollar exchange rate as the largest contributor of connectedness to the other series. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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22 pages, 1867 KiB  
Article
Applications of the Investor Sentiment Polarization Model in Sudden Financial Events
by Yuanyuan Yu, Hongjia Wei and Tinggui Chen
Systems 2022, 10(3), 75; https://doi.org/10.3390/systems10030075 - 5 Jun 2022
Viewed by 2126
Abstract
At present, the proportion of individual financial investors in China is relatively high, the phenomenon of noisy trading is frequent, and the market system risk caused by the polarization of investor sentiment cannot be ignored. Therefore, exploring the polarization of investor sentiment under [...] Read more.
At present, the proportion of individual financial investors in China is relatively high, the phenomenon of noisy trading is frequent, and the market system risk caused by the polarization of investor sentiment cannot be ignored. Therefore, exploring the polarization of investor sentiment under the influence of sudden financial events is of great practical significance for alleviating abnormal fluctuations in financial markets and building a long-term and stable market mechanism. Based on the B–A scale-free network and J–A model, this paper combines the multi-agent system and the DSSW model to construct a polarization model of investor sentiment. Through simulation tests and empirical tests, it is concluded that the polarization of investor sentiment stems from the herd effect and exclusion effect of investor behavior, and that increasing the coefficient of destabilization ε and reducing the effect interval threshold D1 and D2 will aggravate the polarization of investor sentiment in the equilibrium state, while increasing the effect parameter α and β will not affect the polarization of investor sentiment in the equilibrium state, but will accelerate the number of interactions required to reach the equilibrium state. Finally, this paper puts forward targeted policy recommendations to provide references for responding to unexpected financial events. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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25 pages, 4087 KiB  
Article
Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment
by Haider Maqsood, Muazzam Maqsood, Sadaf Yasmin, Irfan Mehmood, Jihoon Moon and Seungmin Rho
Systems 2022, 10(2), 24; https://doi.org/10.3390/systems10020024 - 23 Feb 2022
Cited by 9 | Viewed by 4981
Abstract
Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data [...] Read more.
Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data analytics for analyzing stock exchange markets. This is due to the fact that the sentiments around major global events like Brexit or COVID-19 significantly affect business decisions and investor perceptions, as well as transactional trading statistics and index values. Hence, in this research, we examined a case study from the Brexit event to assess the influence that feelings on the subject have had on the stock markets of European Union (EU) nations. Brexit has implications for Britain and other countries under the umbrella of the European Union (EU). However, a common point of debate is the EU’s contribution preferences and benefit imbalance. For this reason, the Brexit event and its impact on stock markets for major contributors and countries with minimum donations need to be evaluated accurately. As a result, to achieve accurate analysis of the stock exchanges of different EU nations from two different viewpoints, i.e., the major contributors and countries contributing least, in response to the Brexit event, we suggest an optimal deep learning and machine learning model that incorporates social media sentiment analysis regarding Brexit to perform stock market prediction. More precisely, the machine learning-based models include support vector machines (SVM) and linear regression (LR), while convolutional neural networks (CNNs) are used as a deep learning model. In addition, this method incorporates around 1.82 million tweets regarding the major contributors and countries contributing least to the EU budget. The findings show that sentiment analysis of Brexit events using a deep learning model delivers better results in comparison with machine learning models, in terms of root mean square values (RMSE). The outcomes of stock exchange analysis for the least contributing nations in relation to the Brexit event can aid them in making stock market judgments that will eventually benefit their country and improve their poor economies. Likewise, the results of stock exchange analysis for major contributing nations can assist in lowering the possibility of loss in relation to investments, as well as helping them to make effective decisions. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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16 pages, 3858 KiB  
Article
Evolutionary Game of Small and Medium-Sized Enterprises’ Accounts-Receivable Pledge Financing in the Supply Chain
by Haiju Hu, Yakun Li, Mao Tian and Xinjiang Cai
Systems 2022, 10(1), 21; https://doi.org/10.3390/systems10010021 - 17 Feb 2022
Cited by 8 | Viewed by 3941
Abstract
Due to limited guarantees, it is difficult for small and medium-sized enterprises (SMEs) to obtain loans from banks. Supply chain accounts-receivable pledge financing (SCARPF) can help in overcoming those financing difficulties. This study developed an evolutionary game model of banks, core enterprises and [...] Read more.
Due to limited guarantees, it is difficult for small and medium-sized enterprises (SMEs) to obtain loans from banks. Supply chain accounts-receivable pledge financing (SCARPF) can help in overcoming those financing difficulties. This study developed an evolutionary game model of banks, core enterprises and SMEs in SCARPF, analyzed the evolution path and evolution rules of the model, and performed a numerical simulation. The results indicated that the result of the evolutionary game depends on the initial values of the variables. When certain conditions are met, the system will evolve to (lending, keep the contract). The higher the return rate during either normal production of SMEs, the loan interest rate or supply chain punishment, the more likely it is that banks will lend money and SMEs will keep the contract. However, the bank will only be likely to lend money, enabling SMEs to keep the contract, when the probability of core enterprises and SMEs engaging in joint loan fraud—or the proportion of the benefits that SMEs share when engaging in joint loan fraud—is reduced. The results of this study provide insights for banks, core enterprises, and SMEs in supply chain financing decisions, which is conducive to solving the financing difficulties of SMEs. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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19 pages, 2491 KiB  
Article
Measuring Bank Systemic Risk in China: A Network Model Analysis
by Jin Zou, Xu Fu, Jun Yang and Chi Gong
Systems 2022, 10(1), 14; https://doi.org/10.3390/systems10010014 - 8 Feb 2022
Cited by 5 | Viewed by 3876
Abstract
Correlation networks and risk spillovers within financial institutions contribute to the generation and dissemination of systemic risk. In this paper, a risk correlation network is constructed among Chinese banks employing the maximum entropy method, which simulates the individual risks of banks in the [...] Read more.
Correlation networks and risk spillovers within financial institutions contribute to the generation and dissemination of systemic risk. In this paper, a risk correlation network is constructed among Chinese banks employing the maximum entropy method, which simulates the individual risks of banks in the presence of exogenous shocks, the contagious risks, and total systemic risk through the effect of network spillovers, and analyzes its influencing factors. The results show that there is an increasingly rising trend in the overall systemic risk of China’s banking industry, and that the value of systemic risk is relatively large. From the perspective of the composition of banking systemic risk, individual risk accounts for a large proportion, about 70%, which is the main source of banking systemic risk, among which China’s state-owned commercial banks are the largest source. The contagious risk of banks accounts for about 30%. Furthermore, the contagious risk contribution of various banks is basically negatively correlated with their scale. The smallest urban commercial bank in the banking industry contributes at least 50% of the contagion risk, while the state-owned commercial bank, which accounts for about 40% of the total assets of the banking industry, only contributes less than 30% of the contagion risk. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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11 pages, 457 KiB  
Article
Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches
by Sarah Bai and Yijun Zhao
Systems 2021, 9(3), 55; https://doi.org/10.3390/systems9030055 - 22 Jul 2021
Cited by 8 | Viewed by 6597
Abstract
This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard [...] Read more.
This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard based on quantitative and qualitative characteristics of the companies. Collaborating with the management team of Rose Street Capital (RSC), we explore the most influential factors of their balanced scorecard using their retrospective investment decisions of successful and failed startup companies. Our study employs six standard machine learning models and their counterparts with an additional feature selection technique. Our findings suggest that “planning strategy” and “team management” are the two most determinant factors in the firm’s investment decisions, implying that qualitative factors could be more important to startup evaluation. Furthermore, we analyzed which machine learning models were most accurate in predicting the firm’s investment decisions. Our experimental results demonstrate that the best machine learning models achieve an overall accuracy of 78% in making the correct investment decisions, with an average of 87% and 69% in predicting the decision of companies the firm would and would not have invested in, respectively. Our study provides convincing evidence that qualitative criteria could be more influential in investment decisions and machine learning models can be adapted to help provide which values may be more important to consider for a venture capital firm. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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